Observe that when \(0\%\)  of the training labels are flipped (i.e., the original dataset), a general trend in terms of accuracy can be observed: CART \(<\)  OCT \(<\)  OCT-H \(<\)  OCTSVM, with the only exception of Wholesale in which CART obtains a non-meaningful slightly larger average accuracy. We would like to emphasize that these results are not surprising. Indeed, on the one hand, OCT is an optimal classification version of the CART algorithm, and hence better results should be expected from this model, as already shown in the computational experience in [1]}, and also evidenced in our experiments. Moreover, OCT is just a restricted version of OCT-H, in that in the latter, the set of admissible hyperplanes is larger than in the former. Also, as already pointed out in Fig. REF , OCTSVM goes one step further and, apart from allowing oblique hyperplanes based trees, has a more solid structure due to the margin maximization, the distance based errors and the possibility of relabeling points. All together turns into higher accuracy results as pointed out in our experiments. In some datasets the comparison of the different methods gives rather similar results, however in some others OCTSVM is above a 5\(\%\)  percent of accuracy with respect to the other three methods.
